As AI becomes more central to problems in every industry, and the power of AI applications become more apparent, there’s greater pressure than ever on CTOs, senior developers, and entire technology organizations to develop AI solutions that not only perform well, but are trustworthy, transparent, and ethical. If you’re interested in a quick primer on how to think about interpretability and explainability during the AI implementation process, consider these nine points below (for a deeper dive, read the full white paper).
1. Use simple outputs for self-interpretable models
The first way to increase transparency of your AI solution is to use the simplest possible model for solving the problem. Models like linear regression have some built-in outputs that help humans understand their predictions with very little added effort.
The easiest way to interpret a model like linear regression is to visualize its line of best fit, which can help you not only understand where it will place future predictions, but also help you visualize how that line of best fit relates to individual data points.
You can also output the coefficient of that line of best fit with a single line of code, giving you a very succinct numerical explanation for the relationship between two variables.
Ex. of a linear regression model
2. Dig Into confusion matrices and classification reports
The next easiest way to be more transparent with your AI implementation is to simply practice good data science. While it may sound simple, it is not always easy to implement.
Confusion matrices are a data scientist’s best friend when it comes to classification problems, and they help not only the machine learning practitioner in fine-tuning models for specific outcomes, but they can also help whole organizations understand what types of errors the model is prone to make (false positives, false negatives, etc.) and how often it makes those types of errors.
Metrics for model accuracy, and more specific performance metrics like precision, recall and f1 score let stakeholders know that they can trust a model’s outputs and how far that trust should go.
3. Visualize, visualize, visualize
Sometimes it's easier to trust something when we can see it. Some models, like decision trees, have built-in visualizations that allow humans to not only illustrate the entire model’s decision making process, but track a single input along the tree’s branches and leaves to the eventual prediction.
These visualizations are the gold standard for understanding and trusting a model, but are only available to specific types of models, and are quickly impractical as problems – and the AI models used to solve them – get more complex.
Ex. of a decision tree visualization
4. Know what methods are available for a given model
This is where the PHDs working in the field of AI transparency, explainability, and interpretability start truly earning their paychecks. For models like random forests, visualizations become too large and difficult to follow, so some very smart people have started to develop explainability methods that can understand how specific features impact a model’s decision making process, and can output those relative values in a simple, human-readable format.
One example of these explainability methods is the importance score, or, as it's known among the more technical community, permutation feature importance.
Ex. of an importance score
5. Understand the difference between a model and an algorithm
“Model” and “algorithm” tend to be used interchangeably at times, but simple machine learning models like decision trees and even random forests are a far cry from AI algorithms like Chat-GPT. But where is the cut-off? “Models” become “algorithms” when multiple models are used together, either sequentially or in tandem, with specific rules for how information should flow from one to another.
Algorithms like GridSearch run a machine learning model multiple times to put the best version of that model in place, while other processes – like gradient boosting – string multiple models together to increase accuracy. Understanding how different types of algorithms affect transparency is key to balancing the complexity-transparency trade-off in AI.
6. Explain model decision making processes as a whole
When AI becomes too complex for humans to easily understand its general “thought process,” as in the case with random forests, global model-agnostic methods become extremely important for transparency, like importance score.
Other examples of methods that can explain the relative importance of specific features to an AI solution’s decision making process as a whole – but are not specific to one type of model – include Partial Dependence Plots (PDPs), Accumulated Local Effect (ALE) visualizations, and a few others. These global model-agnostic methods start to form a common foundation for explaining the decision making processes of nearly any AI implementation.
7. Explain individual model decisions
The complements to global methods, like the ones listed above, are local methods. Local model-agnostic methods don’t seek to explain how the model uses the entire dataset to make all of its predictions. Instead, they focus on a single decision, or each individual prediction of the AI solution, helping data scientists and stakeholders alike understand what goes into key decisions, or holding up individual examples of outputs for scrutiny.
Local method examples include: Individual Conditional Expectation (ICE), SHAPely additive explanation (SHAP), and Local Interpretable Model-agnostic Explanation (LIME).
Ex. of a PDP model
8. Visualize some more
Natural Language Processing, Neural Networks, Deep Learning, and Large Language Models are poster-children for opaque, poorly understood AI, but they don’t have to be. Self-attention mechanisms, a key component of many language models, including some very large, pre-trained ones, can be visualized with a few lines of code.
The only problem is that there are so many visualizations that you can generate from even a simple neural network and there is either too much information for a human to process or the visualizations are overly succinct (out of necessity), and not displaying all the information. These types of visualizations can also be computationally taxing (read: slow and/or expensive; oftentimes both).
9. Be deliberate about choosing AI solutions
Luckily, there’s a simple, elegant way to implement transparent AI with very few drawbacks, and that’s to be very focused with the problem your AI is meant to solve. Much of the difficulty in transparency comes with larger, foundational models, since they are trained on so much irrelevant data, they become too complex for the best transparency methods to apply.
With a more purposeful approach, AI models can be trained on only relevant data, with a specific task in mind, and as a result, more focused AI models are not only more transparent, but they can also outperform larger, flashier foundational models.
If you are looking to find out more about what purposeful, focused AI can do for your site search experience, schedule a demo of Algolia’s AI Search solution now at algolia.com/demorequest, or download the full white paper to learn more about AI transparency.
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